Automatic identification of epileptic electroencephalography signals using higher-order spectra

Epilepsy is a pathological condition characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behaviour of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on electroencephalography (EEG) recordings. The use of non-linear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal), and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model classifier and a support vector machine classifier. Results show that the classifiers were able to achieve 93.11 per cent and 92.67 per cent classification accuracy respectively, with selected HOS-based features. About 2 h of EEG recordings from ten patients were used in this study.